SoroushVahidi/scidocs huggingface dataset:
Intelligent stock data prediction using predictive data mining techniques
(2016)

Cloud computing is the one of the admired paradigms of current era, which facilitates the users with on demand services and pay as you use services. It has tremendous applications in almost every sphere such as education, gaming, social networking, transportation, medical, business, stock market, pattern matching, etc. Stock market is such an industry where lots of data is generated and benefits are reaped on the basis of accurate prediction. So prediction is a vital part of stock market. Therefore, an attempt is being made to predict the stock market based on the given data set of stock market along with some features; using the techniques available for predictive data mining. Machine learning is one of the upcoming trends of data mining; hence few machine learning algorithms have been used such as Decision tree, Linear model, Random forest and further their results have been compared using the classification evaluation parameters such as H, AUC, ROC, TPR, FPR, etc. Random forest have been consider as the most effective model as it yield the highest accuracy of 54.12% whereas decision tree and linear model gives the accuracy of 51.87% and 52.83% respectively.
New VF-power system architecture and evaluation for future aircraft
(2006)

Conventional aircraft power system is a constant-frequency (CF) supply based on mechanical-regulated constant-speed mechanism that has relatively low efficiency. Replacing the CF system with variable-frequency (VF) power improves overall efficiency, and reduce the system's weight and volume. However, this creates a new tier of requirements and design challenges. Novel VF-power architecture is developed with minimization of the power losses throughout the stages of power conversions. Optimal partitioning and grouping of onboard AC loads has been discussed with specific system data. New VF-input multi-functional power converters are also briefly discussed.
A State-of-the-Art of Semantic Change Computation
(2018)

This paper reviews state-of-the-art of one emerging field in computational linguistics — semantic change computation, proposing a framework that summarizes the literature by identifying and expounding five essential components in the field: diachronic corpus, diachronic word sense characterization, change modelling, evaluation data and data visualization. Despite the potential of the field, the review shows that current studies are mainly focused on testifying hypotheses proposed in theoretical linguistics and that several core issues remain to be solved: the need for diachronic corpora of languages other than English, the need for comprehensive evaluation data for evaluation, the comparison and construction of approaches to diachronic word sense characterization and change modelling, and further exploration of data visualization techniques for hypothesis
A DC-90 GHz 4-Vpp differential linear driver in a 0.13 μm SiGe:C BiCMOS technology for optical modulators
(2017)

In this paper, a linear driver for optical modulators in a 0.13 μm SiGe:C BiCMOS technology with fT/fmax of 300/500 GHz is presented. The driver is implemented following a distributed amplifier topology in a differential manner. In a 50-Ω environment, the circuit delivers a maximum differential output amplitude of 4 Vpp, featuring a small-signal gain of 13 dB and 3-dB bandwidth of 90 GHz. Time-domain measurements using OOK (up to 56 Gb/s) and PAM-4 (at 30Gbaud) are performed, demonstrating the maximum output swing and linearity of the driver. The output power to power dissipation ratio is 3.6%. To the best knowledge of the authors, this is the first time a linear driver for optical modulators demonstrates such bandwidth.
FENNEL: streaming graph partitioning for massive scale graphs
(2014)

Balanced graph partitioning in the streaming setting is a key problem to enable scalable and efficient computations on massive graph data such as web graphs, knowledge graphs, and graphs arising in the context of online social networks. Two families of heuristics for graph partitioning in the streaming setting are in wide use: place the newly arrived vertex in the cluster with the largest number of neighbors or in the cluster with the least number of non-neighbors.
 In this work, we introduce a framework which unifies the two seemingly orthogonal heuristics and allows us to quantify the interpolation between them. More generally, the framework enables a well principled design of scalable, streaming graph partitioning algorithms that are amenable to distributed implementations. We derive a novel one-pass, streaming graph partitioning algorithm and show that it yields significant performance improvements over previous approaches using an extensive set of real-world and synthetic graphs.
 Surprisingly, despite the fact that our algorithm is a one-pass streaming algorithm, we found its performance to be in many cases comparable to the de-facto standard offline software METIS and in some cases even superiror. For instance, for the Twitter graph with more than 1.4 billion of edges, our method partitions the graph in about 40 minutes achieving a balanced partition that cuts as few as 6.8% of edges, whereas it took more than 81/2 hours by METIS to produce a balanced partition that cuts 11.98% of edges. We also demonstrate the performance gains by using our graph partitioner while solving standard PageRank computation in a graph processing platform with respect to the communication cost and runtime.
A Language Modeling Approach to Predicting Reading Difficulty
(2004)

We demonstrate a new research approach to the problem of predicting the reading difficulty of a text passage, by recasting readability in terms of statistical language modeling. We derive a measure based on an extension of multinomial naïve Bayes classification that combines multiple language models to estimate the most likely grade level for a given passage. The resulting classifier is not specific to any particular subject and can be trained with relatively little labeled data. We perform predictions for individual Web pages in English and compare our performance to widely-used semantic variables from traditional readability measures. We show that with minimal changes, the classifier may be retrained for use with French Web documents. For both English and French, the classifier maintains consistently good correlation with labeled grade level (0.63 to 0.79) across all test sets. Some traditional semantic variables such as type-token ratio gave the best performance on commercial calibrated test passages, while our language modeling approach gave better accuracy for Web documents and very short passages (less than 10 words).
Dynamic projection mapping onto a deformable object with occlusion based on high-speed tracking of dot marker array
(2015)

In recent years, projection mapping has attracted much attention in a variety of fields. Generally, however, the objects in projection mapping are limited to rigid and static or quasistatic objects. Dynamic projection mapping onto a deformable object could remarkably expand the possibilities. In order to achieve such a projection mapping, it is necessary to recognize the deformation of the object even when it is occluded. However, it is still a challenging problem to achieve this task in real-time with low latency. In this paper, we propose an efficient, high-speed tracking method utilizing high-frame-rate imaging. Our method is able to track an array of dot markers arranged on a deformable object even when there is external occlusion caused by the user interaction and self-occlusion caused by the deformation of the object itself. Additionally, our method can be applied to a stretchable object. Dynamic projection mapping with our method showed robust and consistent display onto a sheet of paper and cloth with a tracking performance of about 0.2 ms per frame, with the result that the projected pattern appeared to be printed on the deformable object.
Novel Online Methods for Time Series Segmentation
(2008)

To efficiently and effectively mine massive amounts of data in the time series, approximate representation of the data is one of the most commonly used strategies. Piecewise linear approximation is such an approach, which represents a time series by dividing it into segments and approximating each segment with a straight line. In this paper, we first propose a new segmentation criterion that improves computing efficiency. Based on this criterion, two novel online piecewise linear segmentation methods are developed, the feasible space window method and the stepwise feasible space window method. The former usually produces much fewer segments and is faster and more reliable in the running time than other methods. The latter can reduce the representation error with fewer segments. It achieves the best overall performance on the segmentation results compared with other methods. Extensive experiments on a variety of real-world time series have been conducted to demonstrate the advantages of our methods.
Broadcast Encryption with Traitor Tracing
(2013)

In this thesis, we look at definitions and black-box constructions with efficient instantiations for broadcast encryption and traitor tracing. We begin by looking at the security notions for broadcast encryption found in the literature. Since there is no easy way to compare these existing notions, we propose a framework of security notions for which we establish relationships. We then show where existing notions fit within this framework. Second, we present a black-box construction of a decentralized dynamic broadcast encryption scheme. This scheme does not rely on any trusted authorities, and new users can join at any time. It achieves the strongest security notion based on the security of its components and has an efficient instantiation that is fully secure under the DDH assumption in the standard model. Finally, we give a black-box construction of a message-based traitor tracing scheme, which allows tracing not only based on pirate decoders but also based on watermarks contained in a message. Our scheme is the first one to obtain the optimal ciphertext rate of 1 asymptotically. We then show that at today’s data rates, the scheme is already practical for standard choices of values.
Cross-domain security of cyber-physical systems
(2017)

The interaction between the cyber domain and the physical domain components and processes can be leveraged to enhance the security of the cyber-physical system. In order to do so, we must first analyze various cyber domain and physical domain information flows, and characterize the relation between them using model functions. In this paper, we present a notion of cross-domain security of cyber-physical systems, whereby we present a security analysis framework that can be used for generating novel cross-domain attack models, attack detection methods, etc. We demonstrate how information flows such as discrete domain signal flows and continuous domain energy flows in the cyber and physical domain can be used to generate model functions using data-driven estimation, and use this model functions for performing various cross-domain security analysis. We also demonstrate the practical applicability of the cross-domain security analysis framework using the cyber-physical manufacturing system as a case study.
Facial reconstruction: soft tissue thickness values for South African black females.
(2011)

In forensic science, investigators frequently have to deal with unidentified skeletonised remains. When conventional methods of identification are unsuccessful, forensic facial reconstruction (FFR) may be used, often as a last resort, to assist the process. FFR relies on the relationships between the facial features, subcutaneous soft tissues and underlying bony structure of the skull. The aim of this study was to develop soft tissue thickness (STT) values for South African black females for application to FFR, to compare these values to existing literature or databases and to add these values to existing population data. Computerised tomography scanning was used to determine average population-specific STT values at 28 facial landmarks of 154 black females. Descriptive statistics are provided for these STT values, which were also compared to those reported in three other comparable databases. Many of these STT values are significantly different from those reported for comparable groups, suggesting that individuals from different geographical areas have unique facial features thus requiring population-specific STT values. Repeatability tests indicated that most measurements could be recorded with a high degree of reliability.
Fine-Grained Visual Comparisons with Local Learning
(2014)

Given two images, we want to predict which exhibits a particular visual attribute more than the other-even when the two images are quite similar. Existing relative attribute methods rely on global ranking functions; yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering. To address these issues, we propose a local learning approach for fine-grained visual comparisons. Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons. We show how to identify these analogous pairs using learned metrics. With results on three challenging datasets-including a large newly curated dataset for fine-grained comparisons-our method outperforms stateof-the-art methods for relative attribute prediction.
Spatial Correlation and Mobility-Aware Traffic Modeling for Wireless Sensor Networks
(2009)

Recently, there has been a great deal of research on using mobility in wireless sensor networks (WSNs) to facilitate surveillance and reconnaissance in a wide deployment area. Besides providing an extended sensing coverage, node mobility along with spatial correlation introduces new network dynamics, which could lead to the traffic patterns fundamentally different from the traditional (Markovian) models. In this paper, a novel traffic modeling scheme for capturing these dynamics is proposed that takes into account the statistical patterns of node mobility and spatial correlation. The contributions made in this paper are twofold. First, it is shown that the joint effects of mobility and spatial correlation can lead to bursty traffic. More specifically, a high mobility variance and small spatial correlation can give rise to pseudo-long-range-dependent (LRD) traffic (high bursty traffic), whose autocorrelation function decays slowly and hyperbolically up to a certain cutoff time lag. Second, due to the ad hoc nature of WSNs, certain relay nodes may have several routes passing through them, necessitating local traffic aggregations. At these relay nodes, our model predicts that the aggregated traffic also exhibits the bursty behavior characterized by a scaled power-law decayed autocovariance function. According to these findings, a novel traffic shaping protocol using movement coordination is proposed to facilitate effective and efficient resource provisioning strategy. Finally, simulation results reveal a close agreement between the traffic pattern predicted by our theoretical model and the simulated transmissions from multiple independent sources, under specific bounds of the observation intervals.
A Review of Facebook Research in the Social Sciences.
(2012)

With over 800 million active users, Facebook is changing the way hundreds of millions of people relate to one another and share information. A rapidly growing body of research has accompanied the meteoric rise of Facebook as social scientists assess the impact of Facebook on social life. In addition, researchers have recognized the utility of Facebook as a novel tool to observe behavior in a naturalistic setting, test hypotheses, and recruit participants. However, research on Facebook emanates from a wide variety of disciplines, with results being published in a broad range of journals and conference proceedings, making it difficult to keep track of various findings. And because Facebook is a relatively recent phenomenon, uncertainty still exists about the most effective ways to do Facebook research. To address these issues, the authors conducted a comprehensive literature search, identifying 412 relevant articles, which were sorted into 5 categories: descriptive analysis of users, motivations for using Facebook, identity presentation, the role of Facebook in social interactions, and privacy and information disclosure. The literature review serves as the foundation from which to assess current findings and offer recommendations to the field for future research on Facebook and online social networks more broadly.
Information Disclosure and Control on Facebook: Are They Two Sides of the Same Coin or Two Different Processes?
(2009)

Facebook, the popular social network site, is changing the nature of privacy and the consequences of information disclosure. Despite recent media reports regarding the negative consequences of disclosing information on social network sites such as Facebook, students are generally thought to be unconcerned about the potential costs of this disclosure. The current study explored undergraduate students' information disclosure and information control on Facebook and the personality factors that influence levels of disclosure and control. Participants in this online survey were 343 undergraduate students who were current users of Facebook. Results indicated that participants perceived that they disclosed more information about themselves on Facebook than in general, but participants also reported that information control and privacy were important to them. Participants were very likely to have posted information such as their birthday and e-mail address, and almost all had joined an online network. They were also very likely to post pictures such as a profile picture, pictures with friends, and even pictures at parties and drinking with friends. Contrary to expectations, information disclosure and information control were not significantly negatively correlated, and multiple regression analyses revealed that while disclosure was significantly predicted by the need for popularity, levels of trust and self-esteem predicted information control. Therefore, disclosure and control on Facebook are not as closely related as expected but rather are different processes that are affected by different aspects of personality. Implications of these findings and suggestions for future research are discussed.
On the evolution of user interaction in Facebook
(2009)

Online social networks have become extremely popular; numerous sites allow users to interact and share content using social links. Users of these networks often establish hundreds to even thousands of social links with other users. Recently, researchers have suggested examining the activity network - a network that is based on the actual interaction between users, rather than mere friendship - to distinguish between strong and weak links. While initial studies have led to insights on how an activity network is structurally different from the social network itself, a natural and important aspect of the activity network has been disregarded: the fact that over time social links can grow stronger or weaker. In this paper, we study the evolution of activity between users in the Facebook social network to capture this notion. We find that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages. For example, only 30% of Facebook user pairs interact consistently from one month to the next. Interestingly, we also find that even though the links of the activity network change rapidly over time, many graph-theoretic properties of the activity network remain unchanged.
Spectral collaborative filtering
(2018)

Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the spectral domain, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly out-performs state-of-the-art models. Code and data are available at https://github.com/lzheng21/SpectralCF.
Eco-Routing Navigation System Based on Multisource Historical and Real-Time Traffic Information
(2012)

Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin-destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed.